#TASK 1 GETTING STARTED
print("Hello, R!")
## [1] "Hello, R!"
sessionInfo()
## R version 4.4.3 (2025-02-28 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows 10 x64 (build 19045)
##
## Matrix products: default
##
##
## locale:
## [1] LC_COLLATE=English_United States.utf8
## [2] LC_CTYPE=English_United States.utf8
## [3] LC_MONETARY=English_United States.utf8
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.utf8
##
## time zone: Asia/Karachi
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## loaded via a namespace (and not attached):
## [1] digest_0.6.37 R6_2.6.1 fastmap_1.2.0 xfun_0.51
## [5] cachem_1.1.0 knitr_1.50 htmltools_0.5.8.1 rmarkdown_2.29
## [9] lifecycle_1.0.4 cli_3.6.4 sass_0.4.9 jquerylib_0.1.4
## [13] compiler_4.4.3 tools_4.4.3 evaluate_1.0.3 bslib_0.9.0
## [17] yaml_2.3.10 rlang_1.1.5 jsonlite_2.0.0
library(readxl) library(dplyr) library(ggplot2) library(caret)
# TASK 2 Working with Data Imports
``` r
data_csv <- read.csv("F:/R_codes/csv_data_iris.csv", header = TRUE, stringsAsFactors = FALSE)
head(data_csv) # Display the first few rows of the dataset
## sepal.length..cm. sepal.width..cm. petal.length..cm. petal.width..cm. target
## 1 5.1 3.5 1.4 0.2 0
## 2 4.9 3.0 1.4 0.2 0
## 3 4.7 3.2 1.3 0.2 0
## 4 4.6 3.1 1.5 0.2 0
## 5 5.0 3.6 1.4 0.2 0
## 6 5.4 3.9 1.7 0.4 0
## class
## 1 setosa
## 2 setosa
## 3 setosa
## 4 setosa
## 5 setosa
## 6 setosa
library(readxl)
iris_data <- read_excel("F:/R_codes/excel_data_iris.xlsx")
head(iris_data)
## # A tibble: 6 × 6
## `sepal length (cm)` `sepal width (cm)` `petal length (cm)` `petal width (cm)`
## <dbl> <dbl> <dbl> <dbl>
## 1 5.1 3.5 1.4 0.2
## 2 4.9 3 1.4 0.2
## 3 4.7 3.2 1.3 0.2
## 4 4.6 3.1 1.5 0.2
## 5 5 3.6 1.4 0.2
## 6 5.4 3.9 1.7 0.4
## # ℹ 2 more variables: target <dbl>, class <chr>
clean_iris <- na.omit(iris) # Remove rows with missing values\
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
filtered_iris <- iris %>% filter(Sepal.Length > 5.5)
summary_data <- iris %>%
group_by(Species) %>%
summarize(mean_sepal_length = mean(Sepal.Length))
print(summary_data)
## # A tibble: 3 × 2
## Species mean_sepal_length
## <fct> <dbl>
## 1 setosa 5.01
## 2 versicolor 5.94
## 3 virginica 6.59
#BAR CHART
library(ggplot2)
ggplot(iris, aes(x = Species, y = Sepal.Length)) +
geom_bar(stat = "summary", fun = "mean") +
labs(title = "Average Sepal Length per Species")
#SCATTER PLOT
ggplot(iris, aes(x = Sepal.Length, y = Petal.Length, color = Species)) +
geom_point() +
labs(title = "Scatter Plot of Sepal vs Petal Length")
#INTERACTIVE VISUALIZATION
library(plotly)
##
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:stats':
##
## filter
## The following object is masked from 'package:graphics':
##
## layout
plot_ly(data = iris, x = ~Sepal.Length, y = ~Petal.Length,
type = "scatter", mode = "markers", color = ~Species)
TASK 5 ADVANCE ANALYSIS
#LINEAR REGRESSION
model <- lm(Petal.Length ~ Sepal.Length, data = iris)
summary(model) # Check model details
##
## Call:
## lm(formula = Petal.Length ~ Sepal.Length, data = iris)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.47747 -0.59072 -0.00668 0.60484 2.49512
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.10144 0.50666 -14.02 <2e-16 ***
## Sepal.Length 1.85843 0.08586 21.65 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8678 on 148 degrees of freedom
## Multiple R-squared: 0.76, Adjusted R-squared: 0.7583
## F-statistic: 468.6 on 1 and 148 DF, p-value: < 2.2e-16
#CLUSTERING
clusters <- kmeans(iris[, 1:4], centers = 3)
iris$Cluster <- as.factor(clusters$cluster)
ggplot(iris, aes(Sepal.Length, Petal.Length, color = Cluster)) +
geom_point()
#MACINE LEARNING WITH CARET
library(caret)
## Loading required package: lattice
model <- train(Species ~ ., data = iris, method = "rf", trControl = trainControl(method = "cv", number = 5))
print(model)
## Random Forest
##
## 150 samples
## 5 predictor
## 3 classes: 'setosa', 'versicolor', 'virginica'
##
## No pre-processing
## Resampling: Cross-Validated (5 fold)
## Summary of sample sizes: 120, 120, 120, 120, 120
## Resampling results across tuning parameters:
##
## mtry Accuracy Kappa
## 2 0.9466667 0.92
## 4 0.9466667 0.92
## 6 0.9466667 0.92
##
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was mtry = 2.
Save Cleaned Data
write.csv(iris, "cleaned_iris.csv", row.names = FALSE)
Conclusion
This report analyzed the Iris dataset, performed exploratory data analysis, visualized relationships, and implemented a Random Forest classification model.